The emergence of WL as a barrier in GNN expressivity has also led to a flurry of work on enhancing their expressivity by means of more general architectures. These include higher-order GNNs inspired by higher-dimensional analogs of WL [1]}, [2]}, unique node identifiers [3]}, [4]}, random node initializations [5]}, [6]}, relational pooling [7]}, incorporating additional information on the graph structure [8]}, [9]}, [10]}, [11]}, [12]}, and more. We refer to [13]} for a comprehensive survey of this line of work.
